1. Accurate detection and grading of pterygium through smartphone by a fusion training model
- Author
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Yuwen Liu, Changsheng Xu, Shaopan Wang, Yuguang Chen, Xiang Lin, Shujia Guo, Zhaolin Liu, Yuqian Wang, Houjian Zhang, Yuli Guo, Caihong Huang, Huping Wu, Ying Li, Qian Chen, Jiaoyue Hu, Zhiming Luo, and Zuguo Liu
- Subjects
Cellular and Molecular Neuroscience ,Ophthalmology ,Sensory Systems - Abstract
Background/aimsTo improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data.MethodTwo datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea.ResultsThe detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients’ smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%).ConclusionOur fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.
- Published
- 2023